No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by Health-Yang · Codex Skill · ★ 245
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is MineEcho safe to install? View the security audit →
MineEcho A local-first Memory OS for personal AI assistants: remember, learn, use skills, and spend fewer tokens. 中文文档 MineEcho is a source-available, local-first AI assistant framework for building private, extensible assistant workflows on top of local services and user-owned knowledge. MineEcho is not meant to be just another chat UI. Its product loop is: Remember user preferences and past work. Learn from imported knowledge through Wiki++ and graph context. Use skills and external AI apps through one routing surface. Save context cost with TokenLess reducers and local metrics. Use MineEcho when a normal chat UI is too forgetful, a RAG app is too passive, and an agent framework is too noisy to run as a long-term personal assistant. Why MineEcho What Makes It Different Me
| Stars | 245 |
| Forks | 27 |
| Language | TypeScript |
| Category | Codex Skill |
| Quality Score | 63.527372794713/100 |
| Last Updated | 2026-06-05 |
| Created | 2026-05-28 |
| Platforms | node |
| Est. Tokens | ~1413k |
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MineEcho is Local-first Memory OS for personal AI assistants with L0-L3 memory, Wiki++ knowledge, skill routing, and TokenLess context compression.. It is categorized as a Codex Skill with 245 GitHub stars.
MineEcho is primarily written in TypeScript. It covers topics such as ai-agent, ai-assistant, knowledge-graph.
You can find installation instructions and usage details in the MineEcho GitHub repository at github.com/Health-Yang/MineEcho. The project has 245 stars and 27 forks, indicating an active community.
The top alternatives to MineEcho on Agent Skills Hub include godmode, omega-memory, lucid-memory. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: